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| from transformers import pipeline | |
| import gradio as gr | |
| from PIL import Image | |
| def classify_img(im): | |
| im = Image.fromarray(im.astype('uint8'), 'RGB') | |
| ans = image_cla(im) | |
| labels = {v["label"]: v["score"] for v in ans} | |
| return labels | |
| def voice2text(audio): | |
| text = voice_cla(audio)["text"] | |
| return text | |
| def text2sentiment(text): | |
| sentiment = text_cla(text)[0]["label"] | |
| return sentiment | |
| def make_block(dem): | |
| with dem: | |
| gr.Markdown(""" | |
| # Ejemplo de `space` multiclassifier: Curso Platzi""") | |
| with gr.Tabs(): | |
| with gr.TabItem("Transcribe audio en español"): | |
| with gr.Row(): | |
| audio = gr.Audio(source="microphone", type="filepath") | |
| transcripcion = gr.Textbox() | |
| b1 = gr.Button("Voz a Texto") | |
| with gr.TabItem("Análisis de sentimiento en español"): | |
| with gr.Row(): | |
| texto = gr.Textbox() | |
| label = gr.Label() | |
| b2 = gr.Button("Texto a Sentimiento") | |
| with gr.TabItem("Clasificación de Imágenes"): | |
| with gr.Row(): | |
| image = gr.Image(label="Carga una imagen aquí") | |
| label_image = gr.Label(num_top_classes=5) | |
| b3 = gr.Button("Clasifica") | |
| b1.click(voice2text, inputs=audio, outputs=transcripcion) | |
| b2.click(text2sentiment, inputs=texto, outputs=label) | |
| b3.click(classify_img, inputs=image, outputs=label_image) | |
| if __name__ == '__main__': | |
| image_cla = pipeline("image-classification", model="microsoft/swin-tiny-patch4-window7-224") | |
| voice_cla = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish") | |
| text_cla = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis") | |
| demo = gr.Blocks() | |
| make_block(demo) | |
| demo.launch() | |